		*GENDER, EDUCATION, RELIGON AND ATTITUDES TOWARD GENDER EQUALITY IN NIGERIA
								
*								Daniel Tuki

*** The replication files (i.e., Data and do-file) are only to be used for replicating the analysis in the paper. They are not to be used for other purposes without prior permission. 				
	
		**DEPENDENT VARIABLE**
	
* (1) Support gender equality (equal_educ): This variable was derived from the question, "A university education is more important for a boy than for a girl," with the rsponses on a five-point scale ranging from "Strongly agree" to "Strongly disagree"
*For easy interpretation of the results, I inverted the numerical values assigned to the response categories by subtracting each of them from 4, allowing for higher values to denote greater support for gender equality (i.e. 4 = "Strongly disagree" and 0 = "Strongly agree.")
gen equal_educ = 4 - agree_gen_educ

* (1a) Support gender equality - binary (bin_equal_educ): This is a dummy variable that takes the value of 1 if the respondent either "Strongly disagree" or "Somewhat disagree" that a university education is more important for a boy than a girl. "Neither agree nor disagree", "Somewhat agree" and "Strongly agree" responses were coded as 0. 
gen bin_equal_educ = 0 
replace bin_equal_educ = 1 if equal_educ == 4
replace bin_equal_educ = 1 if equal_educ == 3
replace bin_equal_educ = . if equal_educ  == . 


//					EXPLANATORY VARIABLE
		
* (2) Educational attainment (educ): This measures the highest level of education accomplished by the respondent. It is measured on a scale with ten ordinal categories ranging from, "0 = No formal schooling" to "9 = Master's degree or higher."



//					CONTROL VARIABLES*
	
* (3) Gender (gender1): This is a dummy variable that takes the value of 1 if the respondent is female and 0 if male. 
	
* (4) Muslim affiliation (relig): This is a dummy variable that takes a value of 1 if the respondent is Muslim and 0 if Christian. 
*Since I focus primarily on christians and Muslims, respondents who belong to neither of these religions were treated as missing observations using the codes below:  
replace relig = . if relig == 2
replace relig = . if relig == 3
*In Kaduna, all the respondents were either Christians or Muslims. However, in Edo, 15 respondents reported having no religion, while 10 respondents belonged to other religions besides the major 2. 
	
* (5) Household Income (income): To generate an ordinal variable ranging from 1 to 4 which shows the income level of the households - With higher values denoting a higher level of income and vice versa:
gen income = . 
*To code respondents who answered "No" to the question asking if their income was less than N11,452 per month as 4
replace income = 4 if hhincapprox_1_rp == 0
*To code respondents who answered "No" to the question asking if their income was less than N10,412 per month as 3
replace income = 3 if hhincapprox_2_rp == 0
*To code respondents who answered "No" to the question asking if their income was less than N6,813 per month as 2
replace income = 2 if hhincapprox_3_rp == 0
*To code the remaining respondents who said "yes" to the question asking if their income was less than N6,813 per month as 1
replace income = 1 if hhincapprox_3_rp == 1

* (6) Marital status (married): This is a binary variable that takes the value of 1 if the respondent is married or has ever been married and 0 otherwise. Divorcees and widows/widowers were categorzed as married because divorce or the death of a spouse does not necessarily do away with familial responsibility, especially if the marriage produced offspring. The original variable from which this binary variable "married" was derived is "marstat"

* (7) Age (age1): This measures the age of the respondent in years. 

* (8) Poverty rate (LGA) (pov_125mea): This measures the proportion of the population in the local government area (LGA) where the respondents reside, who were living below 1.25 US dollars per day in 2010. Since this data is gridded, I computed the relevant statistics using QGIS software. The raw dataset could be accessed at: https://www.worldpop.org/

		
		
//						REGRESSION MODELS

* TABLE 1: OLS regression showing the correlates of gender egalitarian attitudes (Full sample)

*Model 1: Baseline model for Northern Region
regress equal_educ north, cluster(lga_name)
*To obtain the AIC statistics
estat ic

*Model 2: Baseline model for Gender
regress equal_educ gender, cluster(lga_name)
*To obtain the AIC statistics
estat ic

*Model 3: Baseline model for Educational attainment
regress equal_educ educ, cluster(lga_name)
*To obtain the AIC statistics
estat ic

*Model 4: Baseline model for Religious affiliation
regress equal_educ relig, cluster(lga_name)
*To obtain the AIC statistics
estat ic

*Model 5: Considering all the explanatory variables
regress equal_educ north gender educ relig, cluster(lga_name)
*To obtain the AIC statistics
estat ic

*Model 6: Considering the explanatory anbd control variables simultaneously
regress equal_educ north educ gender relig age1 married income pov_125mea, cluster(lga_name)
*To obtain the AIC statistics
estat ic



* TABLE 2: OLS regression showing the correlates of gender egalitarian attitudes (Kaduna only)

*Model 1: Baseline model - Gender
regress equal_educ gender1, cluster(lga_name), if edo == 0
*To obtain the AIC statistics
estat ic

*Model 2: Baseline model - Education
regress equal_educ educ, cluster(lga_name), if edo == 0
*To obtain the AIC statistics
estat ic

*Model 3: Baseline model - Religion
regress equal_educ relig, cluster(lga_name), if edo == 0
*To obtain the AIC statistics
estat ic

*Model 4: All explanatory variables
regress equal_educ gender educ relig, cluster(lga_name), if edo == 0
*To obtain the AIC statistics
estat ic

*Model 5: All explanatory variables and control variables
regress equal_educ gender1 educ relig age1 married income pov_125mea, cluster(lga_name), if edo == 0
*To obtain the AIC statistics
estat ic



* TABLE 3: OLS regression showing the correlates of gender egalitarian attitudes (Edo only)

*Model 1: Baseline model - Gender
regress equal_educ gender1, cluster(lga_name), if edo == 1
*To obtain the AIC statistics
estat ic

*Model 12: Baseline model - Education
regress equal_educ educ, cluster(lga_name), if edo == 1
*To obtain the AIC statistics
estat ic

*Model 3: Baseline model - Religion
regress equal_educ relig, cluster(lga_name), if edo == 1
*To obtain the AIC statistics
estat ic

*Model 4: All explanatory variables
regress equal_educ gender educ relig, cluster(lga_name), if edo == 1
*To obtain the AIC statistics
estat ic

*Model 5: All explanatory variables and control variables
regress equal_educ gender educ relig age1 married income pov_125mea, cluster(lga_name), if edo == 1
*To obtain the AIC statistics
estat ic





//						APPENDIX

* To replicate the regression results reported in tables 1, 2, and 3 using a binary opertionalization of the dependent variable and linear probability model: 


* TABLE A1: LPM model replicating the results in Table 1 (Edo and Kaduna)

*Model 1: Baseline model for Northern Region
regress bin_equal_educ north, cluster(lga_name)
*To obtain the AIC statistics
estat ic

*Model 2: Baseline model for Gender
regress bin_equal_educ gender, cluster(lga_name)
*To obtain the AIC statistics
estat ic

*Model 3: Baseline model for Educational attainment
regress bin_equal_educ educ, cluster(lga_name)
*To obtain the AIC statistics
estat ic

*Model 4: Baseline model for Religious affiliation
regress bin_equal_educ relig, cluster(lga_name)
*To obtain the AIC statistics
estat ic

*Model 5: Considering all the explanatory variables
regress bin_equal_educ north gender educ relig, cluster(lga_name)
*To obtain the AIC statistics
estat ic

*Model 6: Considering the explanatory anbd control variables simultaneously
regress bin_equal_educ north educ gender relig age1 married income pov_125mea, cluster(lga_name)
*To obtain the AIC statistics
estat ic




* TABLE A2: LPM model replicating the results in Table 2 (Kaduna)

*Model 3: Baseline model - Gender
regress bin_equal_educ gender1, cluster(lga_name), if edo == 0
*To obtain the AIC statistics
estat ic

*Model 1: Baseline model - Education
regress bin_equal_educ educ, cluster(lga_name), if edo == 0
*To obtain the AIC statistics
estat ic

*Model 2: Baseline model - Religion
regress bin_equal_educ relig, cluster(lga_name), if edo == 0
*To obtain the AIC statistics
estat ic

*Model 4: All explanatory variables
regress bin_equal_educ gender educ relig, cluster(lga_name), if edo == 0
*To obtain the AIC statistics
estat ic

*Model 5: All explanatory variables and control variables
regress bin_equal_educ gender educ relig age1 married income pov_125mea, cluster(lga_name), if edo == 0
*To obtain the AIC statistics
estat ic



* TABLE A3: LPM model replicating the results in Table 3 (Edo)

*Model 1: Baseline model - Gender
regress bin_equal_educ gender1, cluster(lga_name), if edo == 1
*To obtain the AIC statistics
estat ic

*Model 2: Baseline model - Education
regress bin_equal_educ educ, cluster(lga_name), if edo == 1
*To obtain the AIC statistics
estat ic

*Model 3: Baseline model - Religion
regress bin_equal_educ relig, cluster(lga_name), if edo == 1
*To obtain the AIC statistics
estat ic

*Model 4: All explanatory variables
regress bin_equal_educ gender educ relig, cluster(lga_name), if edo == 1
*To obtain the AIC statistics
estat ic

*Model 5: All explanatory variables and control variables
regress bin_equal_educ gender educ relig age1 married income pov_125mea, cluster(lga_name), if edo == 1
*To obtain the AIC statistics
estat ic



//						SUMMARY STATISTICS

*TABLE A4: SUMMARY STATISTICS (EDO and KADUNA)
summ equal_educ bin_equal_educ north gender educ relig age1 married income pov_125mea


*TABLE A5: SUMMARY STATISTICS (KADUNA ONLY)
summ equal_educ bin_equal_educ north gender educ relig age1 married income pov_125mea if edo == 0


*TABLE A6: SUMMARY STATISTICS (EDO ONLY)
summ equal_educ bin_equal_educ north gender educ relig age1 married income pov_125mea if edo == 1












